Electronic systems, such as controllers, computers, navigation systems, avionic systems, medical equipment, and surveillance systems play an increasingly important role in the lives of all people. And as people's dependence on electronics systems increases, so does the need for methods and tools that predict the time to failure and that diagnose faults accurately in these systems.
Current methods for the prediction and prognosis of the failure of electronic systems, such as analog and mixed signal systems, mainly involve two types of approaches. A first approach uses a canary device. A canary device is a replica of an electronic system or component that has been exposed to accelerated stress levels. Because of this exposure to the accelerated stress levels, the canary device is prone to fail before the failure of the actual working device. Therefore, when placed into service in parallel with the actual working device, the failure of the canary device is an indication that the actual working device is likely to fail soon thereafter.
A second approach for the prediction and prognosis of the failure of electronic systems is referred to as a Life Consumption Monitoring method. In this method, a history of environmental stresses (e.g., thermal, vibration, humidity, etc.) that the device has been exposed to is maintained. This environmental history information is then used in conjunction with physics of failure models to compute actual damage to the circuit and then forecast the remaining life of the device.
These current methods however have several shortcomings. The canary device does not actually predict when the device will fail. The failure of the canary device is at best a rough indication of the health of the actual working device. Moreover, if there is a defect in the actual working device, the actual working device may very well fail before the canary device. Additionally, even if the canary device were a better prognosticator, the canary device increases the cost and space requirements of the electronic system.
The Life Consumption Monitoring method depends on accurate data on environmental stresses. To obtain this data, additional environmental sensors have to be deployed. These sensors increase the cost and complexity of the system. Moreover, the possibility that the sensors may be or become faulty makes the predictions unreliable. The development of the model based on the physics of failure theory is not an easy task and requires specialized resources. In the end, the accuracy of the prediction depends on the accuracy of the model. Consequently, the model has to be validated through detailed experimentations, which is resource intensive.
The art is therefore in need of an alternative method to monitor electronic systems and predict the failure of such systems.